High-dimensional and sparse (HiDS) matrices from recommender systems contain various useful patterns. A latent factor (LF) analysis is highly efficient in grasping these patterns. Stochastic gradient descent (SGD) is a widely adopted algorithm to train an LF model. Can its extensions be capable of further improving an LF models’ convergence rate and prediction accuracy for missing data? To answer this question, this work selects two of representative extended SGD algorithms to propose two novel LF models. Experimental results from two HiDS matrices generated by real recommender systems show that compared standard SGD, extended SGD algorithms enable an LF model to achieve a higher prediction accuracy for missing data of an HiDS matrix, a faster convergence rate, and a larger model diversity.